"""simple docstring"""



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__A :     List[Any]				       =				logging.get_logger(__name__)

__A :     int				       =				{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class 	__lowerCAmelCase					(      _snake_case):




    '''simple docstring'''






    __magic_name__   :			Dict       		=       """gpt_neo"""
    __magic_name__   :			int       		=       ["""past_key_values"""]
    __magic_name__   :			Dict       		=       {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





    def __init__(     self				:	Optional[Any]					,				UpperCamelCase__				:	str=50257					,				UpperCamelCase__				:	Any=2048					,				UpperCamelCase__				:	Dict=2048					,				UpperCamelCase__				:	Optional[int]=24					,				UpperCamelCase__				:	Tuple=[[["global", "local"], 12]]					,				UpperCamelCase__				:	Optional[Any]=16					,				UpperCamelCase__				:	Dict=None					,				UpperCamelCase__				:	List[Any]=256					,				UpperCamelCase__				:	List[str]="gelu_new"					,				UpperCamelCase__				:	List[Any]=0.0					,				UpperCamelCase__				:	Dict=0.0					,				UpperCamelCase__				:	Any=0.0					,				UpperCamelCase__				:	List[str]=0.1					,				UpperCamelCase__				:	Optional[int]=1E-5					,				UpperCamelCase__				:	Any=0.02					,				UpperCamelCase__				:	int=True					,				UpperCamelCase__				:	Tuple=50256					,				UpperCamelCase__				:	Optional[int]=50256					,				**UpperCamelCase__				:	Optional[int]					,				):
          A__	:      List[str]      =vocab_size
          A__	:      Optional[Any]      =max_position_embeddings
          A__	:      List[str]      =hidden_size
          A__	:      Dict      =num_layers
          A__	:      Any      =num_heads
          A__	:      str      =intermediate_size
          A__	:      Tuple      =window_size
          A__	:      Union[str, Any]      =activation_function
          A__	:      Dict      =resid_dropout
          A__	:      Optional[int]      =embed_dropout
          A__	:      str      =attention_dropout
          A__	:      List[str]      =classifier_dropout
          A__	:      Optional[int]      =layer_norm_epsilon
          A__	:      str      =initializer_range
          A__	:      str      =use_cache

          A__	:      Optional[Any]      =bos_token_id
          A__	:      List[Any]      =eos_token_id

          A__	:      int      =attention_types
          A__	:      Dict      =self.expand_attention_types_params(__snake_case							)

          if len(self.attention_layers							) != self.num_layers:
                raise ValueError(
                    "Configuration for convolutional module is incorrect. "
                    "It is required that `len(config.attention_layers)` == `config.num_layers` "
                    F'''but is `len(config.attention_layers) = {len(self.attention_layers							)}`, '''
                    F'''`config.num_layers = {self.num_layers}`. '''
                    "`config.attention_layers` is prepared using `config.attention_types`. "
                    "Please verify the value of `config.attention_types` argument."							)

          super().__init__(bos_token_id=__snake_case					,				eos_token_id=__snake_case					,				**__snake_case							)





    @staticmethod
    def 					_UpperCAmelCase		(     UpperCamelCase__				:	Tuple							):
          A__	:      Any      =[]
          for item in attention_types:
                for _ in range(item[1]							):
                      attentions.extend(item[0]							)
          return attentions



def 		lowercase (       UpperCamelCase     :     Tuple       ,  UpperCamelCase     :     str       ,  UpperCamelCase     :     Optional[int]       ,  UpperCamelCase     :     Any		):





      """simple docstring"""
      import torch

      A__	:      Any      =input.size()
      A__	:      Union[str, Any]      =len(SCREAMING_SNAKE_CASE__		)
      A__	:      Optional[Any]      =shape[dimension]

      A__	:      str      =torch.arange(0       ,  SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__		)
      A__	:      Any      =torch.div(sizedim - size       ,  SCREAMING_SNAKE_CASE__       ,  rounding_mode="floor"		) + 1
      A__	:      Dict      =torch.arange(SCREAMING_SNAKE_CASE__		) + low_indices[:min_length][:, None]

      A__	:      Optional[Any]      =[slice(SCREAMING_SNAKE_CASE__		)] * rank
      A__	:      Optional[int]      =indices
      A__	:      List[str]      =input[s]

      A__	:      List[Any]      =list(range(0       ,  rank + 1		)		)
      perm.append(perm.pop(dimension + 1		)		)

      return sliced.permute(SCREAMING_SNAKE_CASE__		)



def 		lowercase (       UpperCamelCase     :     Optional[int]       ,  UpperCamelCase     :     List[str]		):





      """simple docstring"""
      import torch

      A__	:      Dict      =torch.arange(1       ,  SCREAMING_SNAKE_CASE__		)
      A__	:      int      =torch.remainder(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__		)
      A__	:      Optional[Any]      =remainders == 0
      A__	:      str      =candidates[divisor_indices]
      A__	:      List[Any]      =torch.max(SCREAMING_SNAKE_CASE__		)
      return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  rounding_mode="floor"		)






class 	__lowerCAmelCase					(      _snake_case):




    '''simple docstring'''






    @property
    def 					_UpperCAmelCase		(     self				:	Dict							):
          A__	:      Dict      =OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}							)
          if self.use_past:
                self.fill_with_past_key_values_(__snake_case					,				direction="inputs"							)
                A__	:      Tuple      ={0: """batch""", 1: """past_sequence + sequence"""}
          else:
                A__	:      Dict      ={0: """batch""", 1: """sequence"""}

          return common_inputs





    @property
    def 					_UpperCAmelCase		(     self				:	Tuple							):
          return self._config.num_heads





    def 					_UpperCAmelCase		(     self				:	List[str]					,				UpperCamelCase__				:	Union[str, Any]					,				UpperCamelCase__				:	Dict = -1					,				UpperCamelCase__				:	Any = -1					,				UpperCamelCase__				:	Optional[Any] = False					,				UpperCamelCase__				:	List[str] = None					,				):
          A__	:      Optional[int]      =super(__snake_case					,				self							).generate_dummy_inputs(
              __snake_case					,				batch_size=__snake_case					,				seq_length=__snake_case					,				is_pair=__snake_case					,				framework=__snake_case							)

          # We need to order the input in the way they appears in the forward()
          A__	:      str      =OrderedDict({"input_ids": common_inputs["input_ids"]}							)

          # Need to add the past_keys
          if self.use_past:
                if not is_torch_available():
                      raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
                else:
                      import torch

                      A__	:      List[str]      =common_inputs["""input_ids"""].shape
                      # Not using the same length for past_key_values
                      A__	:      Optional[Any]      =seqlen + 2
                      A__	:      Optional[int]      =(
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      A__	:      str      =[
                          (torch.zeros(__snake_case							), torch.zeros(__snake_case							)) for _ in range(self.num_layers							)
                      ]

          A__	:      List[Any]      =common_inputs["""attention_mask"""]
          if self.use_past:
                A__	:      Dict      =ordered_inputs["""attention_mask"""].dtype
                A__	:      Dict      =torch.cat(
                    [ordered_inputs["attention_mask"], torch.ones(__snake_case					,				__snake_case					,				dtype=__snake_case							)]					,				dim=1							)

          return ordered_inputs





    @property
    def 					_UpperCAmelCase		(     self				:	int							):
          return 13









'''simple docstring'''


from __future__ import annotations







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if len(SCREAMING_SNAKE_CASE__	) <= 1 or n <= 1:
								return

				insert_next(SCREAMING_SNAKE_CASE__				, n - 1	)
				rec_insertion_sort(SCREAMING_SNAKE_CASE__				, n - 1	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if index >= len(SCREAMING_SNAKE_CASE__	) or collection[index - 1] <= collection[index]:
								return

				# Swaps adjacent elements since they are not in ascending order
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= (
				    collection[index],
				    collection[index - 1],
				)

				insert_next(SCREAMING_SNAKE_CASE__				, index + 1	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						input('Enter integers separated by spaces: ')
				UpperCAmelCase_						:  list[int]				  =						[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)




